CSSS 508, Week 9: Mapping

• ggmap for mashing up maps with ggplot2
• Labeling points and ggrepel
• Mapping with raw ggplot2
• Lab: visualizing restaurant safety over space and time

ggmap is a package that goes with ggplot2 so that you can plot spatial data directly onto map images downloaded from Google Maps, OpenStreetMap, and Stamen Maps.

What this package does for you:

1. Queries servers for a map (get_map()) at the location and scale you want
2. Plots the raster image as a ggplot object
3. Lets you add more ggplot layers like points, 2D density plots, text annotations
4. Additional functions for interacting with Google Maps (e.g. getting distances by bike)

In Week 5, we looked at types of incidents the Seattle Police Department responded to in a single day. Now, we'll look at where those were.

library(tidyverse); library(ggmap)

spd_raw <- read_csv("https://clanfear.github.io/CSSS508/Seattle_Police_Department_911_Incident_Response.csv")

qmplot will automatically set the map region based on your data:

qmplot(data = spd_raw, x = Longitude, y = Latitude, color = I("firebrick"), alpha = I(0.5))

qmap(location = "mary gates hall university of washington", zoom = 15, maptype = "watercolor", source = "stamen")

Both qmplot() and qmap() are wrappers for a function called get_map() that retrieves a base map layer. Some options:

• location = search query or numeric vector of longitude and latitude
• zoom = a zoom level (3 = continent, 10 = city, 21 = building)
• source = "google", "osm", "stamen"
• maptype =
  • Google: "terrain", "terrain-background", "satellite", "roadmap", "hybrid"
  • Stamen: Good artistic/minimal options! "watercolor", "toner", "toner-background", "toner-lite"
• color = "color" or "bw"

qmap(location = "pike place market", zoom = 14, maptype = "toner-background", source = "stamen")

qmap(location = "seattle", zoom = 8, maptype = "terrain", source = "google")

Let's look at locations of incidents near downtown.

# query the map server
downtown_map <- get_map(location = "pike place market", zoom = 14)
# grab the bounding box coordinate data frame
downtown_bb <- attributes(downtown_map)[["bb"]]
# subset the data based on bounding box
downtown_seattle_incidents <- spd_raw %>%
    filter(downtown_bb[["ll.lat"]] <= Latitude &
               Latitude <= downtown_bb[["ur.lat"]] &
               downtown_bb[["ll.lon"]] <= Longitude &
               Longitude <= downtown_bb[["ur.lon"]])

Call qmplot() with no geom(), and then add density layers:

qmplot(data = downtown_seattle_incidents, geom = "blank", x = Longitude, y = Latitude, maptype = "toner-lite", darken = 0.5) + stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = .2, color = NA) + scale_fill_gradient2("Incident concentration", low = "white", mid = "yellow", high = "red")

Let's label the assaults and robberies specifically in downtown:

assaults <- downtown_seattle_incidents %>% mutate(assault_label = ifelse(`Event Clearance Group` %in% c("ASSAULTS", "ROBBERY"), `Event Clearance Description`, "")) %>% filter(assault_label != "")

Now let's plot the events and label these specifically using geom_label() (geom_text() also works without the background/border):

qmplot(data = downtown_seattle_incidents, x = Longitude, y = Latitude, maptype = "toner-lite", color = I("firebrick"), alpha = I(0.5)) + geom_label(data = assaults, aes(label = assault_label))

You can also try geom_label_repel() or geom_text_repel() if you install and load in the ggrepel() package to fix overlaps:

library(ggrepel)
qmplot(data = downtown_seattle_incidents, x = Longitude, y = Latitude, maptype = "toner-lite", color = I("firebrick"), alpha = I(0.5)) + geom_label_repel(data = assaults, aes(label = assault_label), fill = "black", color = "white", segment.color = "black")

It is also common (and easy) to plot geospatial data using standard ggplot2 functions. Just provide x and y coordinates to use to draw points or lines.

You need only provide ggplot the following:

• Longitude for the x aesthetic
• Latitude for the y aesthetic
• Data to fill polygons if making a chloropleth

sea_tract_data <- read_csv("https://raw.githubusercontent.com/clanfear/CSSS508/master/Lectures/Week9/sea_tract_data.csv")

ggplot(sea_tract_data, aes(x=long, y=lat, group = group, fill=con_disdvntg))  +
  geom_polygon()  + scale_fill_gradient(low="white", high="darkred") +
  coord_equal() + geom_path(color = "black", linetype=1) +
  theme(axis.title=element_blank(), axis.text = element_blank(), panel.grid.major = 
          element_blank(), panel.grid.minor = element_blank(), panel.background = 
          element_blank(), axis.ticks = element_blank(), axis.line = element_blank()) +
  labs(title="Concentrated Disadvantage", fill="Disadvantage\n")

Use the Lab/HW 7 template to practice making maps of the restaurant inspection data. Save your work when you're done by emailing it to yourself. If you wish to submit it for bonus points, turn it in via Canvas by midnight on Tuesday the 30th.